System and method for data record selection by application of predictive models and velocity analysis

ABSTRACT

A computer system for selection of data records for forwarding includes one or more data storage devices storing data records including structured and unstructured data, and an analysis hardware server configured to, in cycles, determine sets of data records for predictive model review, extract words and phrases from the unstructured data, apply the predictive model to determine current-cycle scores for the data records of the set, assign the data records to groups in accordance with the current-cycle score, determine a velocity of change in group, and select data records for forwarding to an operation system based at least in part on the determined velocity of change in group.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of copending U.S. patent applicationSer. No. 13/684,783, filed Nov. 26, 2012, entitled “System for Selectionof Data Records Containing Structured and Unstructured Data”, the entiredisclosure of which is incorporated by reference herein for allpurposes.

FIELD OF INVENTION

The present invention relates to computer systems, and particularly tocomputer systems for use in the financial services field, and moreparticularly to computer systems for use in connection with insurancerelating to injuries.

BACKGROUND

Insurance claims arise in a variety of contexts, including insurancecoverage for individuals, or personal lines, such as personal propertycoverage and personal automotive coverage, as well as business insurancecoverage. Examples of categories of business insurance coverage includeclaims relating to employee injuries, including workers compensationclaims, short term disability claims and long term disability claims.These types of claims are often underwritten by insurance companies, orare self-insured by large employers. The costs associated with theseclaims for insurance companies and self-insuring employers include thecost of medical services provided to injured employees, and incomereplacement payments provided to employees during a period when theemployees are unable to perform their customary job duties.

While insurance provides for medical expenses and partial replacement oflost income, generally regardless of responsibility for the injury, insome cases, another party is legally responsible for the injury. Forexample, in the event of an automobile accident, an individual insureddriver or an employee of an insured business may be injured. If thenegligence of another driver caused the automobile accident, then theinsurance company or, in the case of the injured employee, aself-insuring employer, may be entitled to subrogation. By subrogration,the insurance company or self-insured employer stands in the shoes ofthe injured individual and can seek civil damages or a settlement fromthe negligent party, or the negligent party's insurer. Similarly, in theevent of a claim for covered property damage, the property owner'sinsurance coverage may pay a claim to cover the cost of repairs, butthen seek subrogation against a responsible party.

A wide variety of factual circumstances may give rise to a right in theinsurance company or self-insured employer to subrogation. For example,an employee may be injured as a result of malfunctioning equipment. Themalfunction may result from defective design or manufacture of theequipment or a part, or incorrect maintenance by a contractor.Similarly, property of an insured individual may be damaged as a resultof defective manufacture or maintenance of such items as householdappliances, heating, ventilation and air conditioning systems, and otheritems.

If a claim handler identifies that a claim may be suitable forsubrogation, the claim handler refers the claim to an insurance claimsrecovery operation of the insurance company. However, the claim handlermay not accurately identify all claims that have subrogation potential,thereby resulting in an absence of subrogation recovery. For example,the claim handler may be provided with rules such as excluding allinjuries of certain types from consideration for subrogation. On theother hand, if a claim handler refers to a claims recovery operationexcessive numbers of claims with little or no subrogation potential, theresources of the claims recovery operation are misdirected to review ofthose low potential referred claims.

Systems and methods that provide for superior identification ofsubrogation opportunities would be desirable.

SUMMARY

In an embodiment, a computer system for processing data relating todetermination of suitability for subrogation of insurance claimsincludes one or more data storage devices storing: data relating to theinsurance claims, the stored data comprising: structured data andunstructured data, the unstructured data comprising data indicative ofcommunications between claim handlers and one or more other persons;data defining a predictive model for assessing suitability forsubrogation of claims based on review of the structured data and wordsand phrases extracted from the data indicative of the unstructured data.The system further includes at least one processor in communication withthe one or more data storage devices and configured to, on a periodicbasis: determine a set of claims for review including new claims andselected previously-reviewed claims; apply text mining to the dataindicative of the unstructured data to extract words and phrases fromthe unstructured data; and apply the predictive model to the structureddata and the extracted words and phrases from the unstructured data todetermine a subrogation score associated with each of the claims in theset. The one or more processors are further configured to, for each ofthe previously-reviewed claims, determine whether the subrogation scoreis greater than on previous review; and generate a report including atleast some of the new claims and the previously reviewed claims having agreater subrogation score.

In an embodiment, a computer-implemented method for processing data fordetermination of suitability for subrogation of insurance claims,includes on a periodic basis accessing by a processor from one or moredata storage devices data relating to insurance claims to determine aset of claims for review, the selected claims including new claims andpreviously-reviewed claims, the stored data including structured dataand unstructured data, the unstructured data including data indicativeof communications between claim handlers and one or more other persons.The method further includes processing by the processor using textmining the unstructured data to extract words and phrases from theunstructured data, and applying, by the processor, a predictive modelfor assessing suitability for subrogation of claims based on review ofthe structured data and words and phrases extracted from theunstructured data, to the structured data and the extracted words andphrases from the unstructured data to determine a subrogation scoreassociated with each of the claims in the set. The method furtherincludes determining by the processor, for each of thepreviously-reviewed claims, whether the subrogation score is greaterthan on previous review; and generating by the processor a reportincluding at least some of the new claims and the previously reviewedclaims having a greater subrogation score.

In an embodiment, a non-transitory computer-readable storage medium hasstored processor-executable instructions, which instructions, whenexecuted by a processor, cause the processor to: on a periodic basis,access from one or more data storage devices data relating to claims todetermine a set of claims for review, the selected claims including newclaims and previously-reviewed claims, the stored data comprisingstructured data and unstructured data, the unstructured data comprisingdata indicative of communications between claim handlers and otherpersons; process using text mining the data indicative of notes of theunstructured data to extract words and phrases from the unstructureddata; apply a predictive model for assessing suitability for subrogationof claims based on review of the structured data and words and phrasesextracted from the data indicative of notes of the unstructured data, tothe structured data and the extracted words and phrases from theunstructured data to determine a subrogation score associated with eachof the claims in the set; determine, for each of the previously-reviewedclaims, whether the subrogation score is greater than on previousreview; and generate a report including at least some of the new claimsand the previously reviewed claims having a greater subrogation score.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram showing an environment in which a computersystem for processing data relating to assessment of workerscompensation and other claims for subrogation potential may beimplemented.

FIG. 2 is a process flow diagram of an exemplary method for processingclaim data to assess claims for subrogation potential.

FIG. 3 shows an exemplary screen display for use in configuring textanalysis software for use in connection with analyzing unstructured datain a method and system of the invention.

FIG. 4 illustrates an exemplary analysis of unstructured data using atext analysis system in an embodiment of the invention.

FIG. 5 illustrates data items having positive and negative values in anexemplary predictive model of the invention.

FIG. 6 illustrates analysis by a predictive model of three claims in anexemplary embodiment.

FIG. 7 is a schematic diagram of an exemplary computer system foranalyzing subrogation likelihood in an embodiment of the invention.

FIG. 8 is a diagram of an exemplary server computer and associateddatabases and networked devices in an implementation of a method andsystem of the invention.

FIG. 9 is an exemplary graphical display of an analysis of subrogationlikelihood of a set of claims.

DETAILED DESCRIPTION

It is to be understood that the figures and descriptions of the presentinvention have been simplified to illustrate elements that are relevantfor a clear understanding of the present invention, while eliminating,for the purpose of clarity, many other elements found in typicalcomputer systems and methods for processing of data relating toinsurance services and programs such as analysis of data sets,determination of potential for subrogation, and administration ofinsurance claims. Those of ordinary skill in the art may recognize thatother elements and/or steps are desirable and/or required inimplementing the present invention. However, because such elements andsteps are well known in the art, and because they do not facilitate abetter understanding of the present invention, a discussion of suchelements and steps is not provided herein.

In connection with administration of insurance claims, including claimsrelating to property damage, liability and injuries, by way of example,a wide variety of documents are generated. Such documents includedocuments prepared and submitted by claimants, representatives ofclaimants, representatives of policy holders other than claimants, suchas representatives of employers of injured workers, and third parties,such as medical offices, contractors and auto body shops, for example.In addition, claim handlers generally represent an insurance company orself-insured entity employer in dealing with claimants, employers, thirdparties such as contractors and medical service providers and others,and create telephone notes, structured documents and the like. Forexample, claim handlers typically enter extensive notes regardingtelephone conversations with claimants, witnesses, employerrepresentatives, and others. Typically, the principal focus of theefforts of the claim handlers in connection with a claim is to obtaininformation that can be used to determine whether an injury is covered.For example, if the claim is a claim for workers compensation, the factsto be determined by the claim handler may include where the injuryoccurred, including whether the location of the injury was on anemployer's premises or not, the time of day, the working hours of theclaimant on the day of the injury, the relationship of the injuredindividual's activities to employment duties, and the details of theinjury. The notes and other data are stored by a computerized system ina database associated with the claim. The data associated with the claimmay include either or both of structured data and unstructured data,such as file notes in text format.

In an embodiment, a computer system is configured to apply a predictivemodel to determine a likelihood of subrogation potential for insuranceclaims in a claim database. The system may be configured to iterativelyapply the predictive model to claims at intervals. The intervals may beone or more selected aging intervals of the claims, dated from asuitable start date, such as a date of initial review. The predictivemodel may apply values to data among structured indicators and amongtext indicators. Selected structured indicators may have negative andpositive weightings on the subrogation potential of the claim. By way ofexample, speed of reporting of an incident leading to a claim, such asan injury, if a short report lag time from incident to report, may havea positive weight, while a long report lag time from incident to reportmay have a negative weight. Thus, a report lag indicator may have apositive coefficient for a short report lag value and a negativecoefficient for a long report lag value.

A claim complexity indicator may have a positive weighting, greater thanthat of speed of reporting, if a high level of complexity is recorded,or a negative weighting, if a low level of complexity is recorded.Complexity refers to severity of injuries, in general.

The system is further configured to perform text mining of unstructureddata associated with the claims. The unstructured data may include notesof telephone conversations and in-person meetings conducted withclaimants, witnesses, third party service providers such as body shoprepresentatives, employer representatives and others, as well asrecorded telephone conversations converted to text using voicerecognition technology. Certain words identified in the notes may beassociated with a higher value of likelihood of subrogation. By way ofexample, words such as landlord, contractor or supplier tend to indicatea third party and are therefore associated with a higher probability ofsubrogation. On the other hand, some words or phases are associated witha reduced value of subrogation, such as “claim denied.”

The values and variables may be dependent on data related to a type ofclaim, such as property damage, automobile accident, or injury toemployee, or to an injured employee or a covered employer. By way ofexample, if data indicates that the claim relates to a dog bite injurysuffered by an employee, and the employer is of a type that ordinarilyhandles animals, such as a dog groomer or veterinarian, an animal biteinjury decreases the likelihood of subrogation. On the other hand, ifthe data indicates that the employer is not in a category thatordinarily handles animals, an animal bite is associated with anincrease in likelihood of subrogation.

The system may be configured to review each claim file on a periodicbasis to determine any change in subrogation potential values. Inembodiments, the review may be on a basis other than periodic, such asbased on a number of new claims received since the most recent review.

Referring now to FIG. 1, an exemplary system 100 for processing datarelated to assessment of subrogation potential is shown is shown in anexemplary environment. System 100 includes insurance company 105elements, which includes subrogation likelihood determination systemserver 110, which may be in communication via an internal network, suchas an insurance company intranet or local area network, with claimsdatabase 115. Database 115 includes data relating to claims. The datarelating to claims may include data relating to types of claims andstructured data which may be partly particular to the type of claim. Forexample, for claims relating to injured employees, structured data mayinclude employee and employer identities, dates of claim submission andtype of injury. For any claim type, unstructured data related to theclaim may be included in the database. For injury claims, the data mayinclude structured and unstructured data relating to the nature of theinjury, place and time of occurrence, other persons involved, and otherdata. Server 110 is also in communication with predictive model 117,which may include executable computer-readable instructions and storeddata for analysis of claim data from database 115 and determination ofsubrogation potential values. Exemplary users of system 100 includeclaim handler 120 who records claim data via a user device, which claimdata directly or indirectly received, stored and organized in claimsdatabase 115, and subrogation analyst 122 who accesses server 110 via auser device to review and analyze claim data and subrogation likelihooddata and analyses. Results of subrogation analyses provided by server110 may be displayed for subrogation analyst 122. Data relating toclaims identified as having high subrogation value may be furnished toclaim recovery organization server 130. Claim recovery organizationserver 130 may perform data processing services for a claim recoveryorganization of an insurance company, as discussed further below.

The system 100 provides services in the context of employer 140, whichmay be an insured or have an affiliated insured group providing coveragefor employee injuries. The coverage may include workers compensationcoverage, short term or long term disability coverage, or other coverageinvolving treatment for injuries that cause the employee to be disabledand unable to perform the employee's customary employment duties. Thesystem 100 may also perform administrative services for an employer 140that self-insures, or may perform administrative and/or data processingservices on behalf of another insurance entity that underwrites coveragefor employer 140.

Injured employees, such as injured employee 142, may provide informationregarding the circumstances of the injury to claim handler 120, by anysuitable method, including by voice telephone discussion as shown inFIG. 1. Employer representatives may communicate with claim handler 120such as via computer system 144.

Common situations giving rise to subrogation are illustrated. Forexample, employer vehicle 146 has collided with another vehicle 160.Insurance company 162 provides coverage to the owner of vehicle 160.Employer machinery 170, operated by employee 143, was manufactured ormaintained by a third party, which third party has coverage frominsurance company 172. Employee 149 has fallen on the sidewalk ofbuilding 180. The building owner is covered by insurance company 182.

Claim recovery organization server 130 provides data processing servicesrelating to assertion of subrogation claims against insurance company162, insurance company 172 and insurance company 182. Those subrogationclaims include claims identified by analysis by server 110 employingpredictive model 117. Claims may be added to a work queue for a claimrecovery operation staff based on results of analysis by subrogationdetermination server 110. While FIG. 1 illustrates circumstancesrelating to subrogation in the context of insurance company review ofclaims relating to injured employees, it will be appreciated that thesame principles, such as seeking subrogation from insurance companies ofother drivers in the automotive coverage context, or insurance companiesof other parties such as contractors, appliance manufacturers or others,applies to other factual situations and other types of policies.Similarly, a third party administrator may perform the subrogationanalysis function. A third party administrator may be engaged by aninsurance company to perform claims administration functions, which mayinclude evaluation of claims for subrogation. In embodiments, a thirdparty administrator may return a listing of claims to an insurancecompany for processing by a claim recovery operation. For example, athird party administrator computer system may generate data indicativeof subrogation potential for claims and provide output data via anysuitable method to a computer system employed by an insurance companyclaim recovery operation.

In embodiments, a claim recovery operation and a subrogationdetermination system may employ one or more elements of the samecomputer system. For example, both the claim recovery operation and thesubrogation determination system may access computer systems anddatabases for storing and maintaining data relating to insurance claimsdata, such as workers compensation claims. In embodiments, a singlehardware server or other hardware devices including one or moreprocessors may execute one or more modules configured to perform dataprocessing relating to determining subrogation potential of insuranceclaims and one or more modules configured to perform data processingrelating to claim recovery.

In embodiments, a claim recovery operation may perform variousoperations in addition to seeking payments from responsible parties ortheir insurers. By way of example, in connection with claims involvingongoing payments, such as workers compensation claims or long termdisability claims, the claim recovery operation may seek risk transfer.If risk transfer is attained, the responsibility for the payments istransferred to another party. In some situations, the claim recoveryoperation may serve as a settlement adviser to other operations of aninsurance company or third party administrator that are engaged indirect negotiations.

Referring now to FIG. 2, an exemplary process flow of a method of anembodiment that may be performed by claims analysis server 200 usingdata from claims database 210 and logic of predictive model 220. Themethod may be performed on a cycle, such as a daily cycle, or a cycle ofa period of days, such as between one and fifteen days. The method maycommence with identification of data relating to new claims 235, whichinclude all claims newly-added to the database subsequent to the mostrecent review. Claims are added to the database during routineprocessing. In embodiments, a claim creation event, such as an initialreport of an injury by an employee or an employer representative to aninsurance company, generates an initial creation of a claim. There maybe a period of time between the claim creation event and the addition ofthe claim to the database, during which various data verification andother processes may be performed.

The method may further identify data relating to selected claims thatwere reviewed in a prior cycle, or look back claims 238. The selectionlogic 215 for look back claims for a given cycle may exclude any claimsflagged in the database as having previously been referred to a claimrecovery operation. The selection logic for look back claims may selectclaims at one or more selected ages from initial review or another startage. By way of example, the selection logic may select each claim thatis open and not flagged as referred to claim recovery operation on acycle of every 15 days, on a cycle of every 30 days, or on a morefrequent cycle while the claims are relatively new and then lessfrequently, e.g., 15 days, 30 days, 45 days, 60 days, 90 days, 120 days,and excluding claims above a maximum threshold age, such as 150 days,180 days or 270 days. Of course, the thresholds and cycles may beexpressed in any suitable manner, such as calendar months from a claimcreation date associated with the claim.

Upon selection of new claims and application of selection logic 215 forlook back claims, a model universe 240 of claims is determined. Textmining 245 may then be applied to unstructured data relating to claimsin the model universe 240. Text mining 245 may identify certain words orphrases in the data indicative of notes that are pertinent todetermination of suitability for subrogation. Text mining tools may beconfigured with tools to identify misspelled words correctly, as well asother analytical capabilities. Text mining 245 may be implemented byemploying, for example, one of numerous proprietary or open sourcesoftware tools capable of text mining and configured to identify wordsor phrases selected for pertinence to determination of suitability forsubrogation. Exemplary tools are made available by Attensity of PaloAlto, Calif. Other suitable tools include the STATISTICA text miningsoftware tools available from Statsoft, Inc., of Tulsa, Okla., and theRapidMiner open source software suite available via Rapid-I GmbH ofDortmund, Germany. Text mining identifies words and phrases associatedwith each claim in the model universe. The identified words and phrasesare then stored with logical associations with the claims for analysisusing the predictive model. Structured data associated with the selectedclaims is also employed by the predictive model, and may be extractedand stored in a temporary database to used and available for analysis.

The predictive model 220 may be applied to the identified words andphrases extracted from the text mining process and the structured datarelating to the selected claims 250 to determine a set of initialresults in the form of a subrogation suitability score for each of theselected claims in the set 255. The predictive model may determine thesuitability score by identifying for each claim any element ofstructured data or element from text mining that have an associatedpositive or negative factor, and incrementing or decrementing thesubrogation suitability score by the positive or negative factor.

For all previously-reviewed claims, the system compares 260 the currentsubrogation likelihood score to the most recent subrogation likelihoodscore for the same claim. Responsive to determining that the currentsubrogation likelihood score is greater than the prior subrogationlikelihood score, the system identifies the claim for inclusion in areport 270. All new claims are also included in the report. The datarelating to the new claims and identified previously-reviewed claims isprocessed 265 for report formatting.

Responsive to determining that the current subrogation likelihood scoreis not greater than the prior subrogation likelihood score, the systemidentifies the claim for data storage 280, but not for inclusion in areport. The report may be employed for identification of claims suitablefor claim recovery.

Referring to FIG. 3, window 310 is generated on device 300 and isaccessed by an analyst to review and select terms and phrases for use inconnection with text mining. Window 310 displays a list of terms thatmay be designated for selection by a text mining tool. The exemplarylist in window 310 is partially customized for selection of text andphrases relevant to subrogation likelihood determinations. Thus, thephrase “no subro” 311, the phrase “off premises” 312, the phrase“responsible party” 313, the phrase “self-inflicted” 314, the phrase“time of day” 315, and the phrase “vendor” 316 appear in window 310 andindicate terms that may be identified in a text mining process. The term“zero paid” 320 is shown with a further menu of individual variationsthat translate to “zero paid” in the logic of the text mining tool andin tag field 330. Other terms may include variations, not shown, thattranslate to the higher level term, such as “no subro” or “offpremises.”

Referring now to FIG. 4, exemplary free text and related analysis areshown. Box 400 displays free text notes. The text parsing logicidentifies sentences in the free text, including sentence 410. Withinsentence 410, clauses 412, 414 are identified, and text is associatedwith clauses. Suitable tags are applied to each identified word. Inclause 412, the term “goal” is identified as being in the“subject-ACTOR” subcategory of the NP category (corresponding generallyto nouns) and appropriately tagged. Similarly, the term “defend” isidentified as an active subcategory of the VP category, correspondinggenerally to verbs. In clause 414, the term “to” is identified as a“specifier” in the PP category, and has logically associated therewith,below in the hierarchy, the following terms “favorable” and “decree”,which are accordingly tagged as in the “prep_head” subcategory of the“NP” category. The terms “claim” and “denied” are associated together inthe NP category under clause 414, and may be flagged by the predictivemodel. Other examples of hierarchical organization of terms are shown inFIG. 4.

Referring now to FIG. 5, a table 500 is shown indicating exemplaryrelative importance of selected items of structured and unstructureddata and whether their contribution to suitability for subrogation ispositive or negative, in the predictive model 555 applied by subrogationanalysis server 550. In column 510, a relative ranking is shown. Incolumn 520, elements that may have a positive contribution to anindication of suitability are shown. The positive contribution may bedependent on the value of the data associated with the item. Forexample, the first value, CDC, relates to characteristics of the injury,and has a high contribution only for certain data values. For example, aCDC code indicating that the injury is related to an automobile is apositive indicator. The second positive item in column 520, coverage,has a positive contribution only if the coverage data value isindicative of coverage of the claim. Selected text flags, extracted fromunstructured data, are shown at 522, 524, 526 and 528. Thus,identification of the phrases “responsible party” 522, “landlord” 524,“third party” 526 and “contractor” 528 results in a positivecontribution to suitability. Other values vary. The nature of injuryindicator may indicate a higher likelihood or be neutral. For example, anature of injury indicator for a muscle strain may be neutral, as amuscle strain may result from improper lifting behavior rather than fromthird party causes. A nature of injury indicator for an injury indicatorfor a fracture may be positive, as fractures are more associated withincidents such as vehicular accidents that may involve other parties.The average weekly wage is generally correlated with a higher likelihoodof subrogation with higher average weekly wage. A positive coefficientmay be associated with one or more states in which an accident occurred.In embodiments, based on experience, the value of the positivecoefficient may vary depending on the state.

The negative contribution factors in column 540 include particularvalues of certain factors that can have a positive contribution. Thus,the factors coverage, complexity and CDC may have either a positive ornegative contribution, depending on the value. Thus, a CDC valueindicating a repetitive motion injury has a negative contribution, byway of example.

Referring to FIG. 6, table 600 illustrates data values used by anexemplary predictive model according to an embodiment and application ofthe predictive model using the data values to three exemplary claims.Data element column 610 illustrates structured and non-structured dataelements. Structured data elements are shown at 612 and include CDC,complexity, and other values. Non-structured data elements that may beidentified via analysis of text are shown at 615 and include the termslandlord, contractor, responsible party and third party. First claim 620indicates data values and associated coefficients for a first claim.Thus, for example, the data value column 621 indicates data values ofstructured data. The coefficient column 622 illustrates values ofexemplary coefficients associated with the structured data values. Valuecolumn 623 provides the meaning of the particular data value, e.g., CDC12 means a non-public transportation vehicle. First claim 620 includescertain values with positive coefficients in coefficient column 622,such as the type of vehicle, type of coverage and accident state beingFlorida. Of data elements extracted from unstructured data, only the“third party” element has a non-zero value. Thus, text mining of theunstructured data associated with claim 620 did not identify anyinstances of the “landlord,” “no subro” or “contractor” data elements.Based on the coefficients associated with both structured andunstructured data, claim 620 has a relatively high value of likelihoodof suitability for subrogation, as indicated at 630. Certaincoefficients, such as have a value of zero, and thus the associated dataelements do not change the subrogation suitability for the claim.

Second claim 640 relates to an animal attack. Data value column 641indicates values of structured data for second claim 640, such as valuesof Complexity 2 in the complexity field and AWW 3 in the average weeklywage field. Only the CDC field, or type of incident field, which has ananimal attack (workers compensation) indicator, has a positivecoefficient. All of the unstructured data values have a coefficient andvalue of zero, indicating that no instances of the listed data elementswere identified in the text mining of the unstructured data.Accordingly, the value 650 of claim 640 is lower than the value 630determined for first claim 620.

Third claim 660 relates to a motion tendinitis injury. This injury has anegative coefficient 662 in the claim type field. The coverage fieldalso has a negative coefficient 664, associated with the value state actcumulative injury. The only field associated with an unstructured dataelement, the “third party” field, has a positive coefficient 668,indicating that text mining of the unstructured data has identified the“third party” data. The value of subrogation likelihood 670 for thirdclaim 660 is lower than either of first claim 620 or second claim 640.

Referring to FIG. 7, an exemplary computer system 700 for use in animplementation of the invention will now be described. In computersystem 700, processor 710 executes instructions contained in applicationprograms, which in this example include software for implementingpredictive model 725, text mining software 726 and claim filteringsoftware 728, which programs are stored as processor-executableinstructions stored in non-transitory storage media, namely storagedevices 720. As used herein, the term “processor” broadly refers to andis not limited to a single- or multi-core general purpose processor, aspecial purpose processor, a conventional processor, a GraphicsProcessing Unit (GPU), a digital signal processor (DSP), a plurality ofmicroprocessors, one or more microprocessors in association with a DSPcore, a controller, a microcontroller, one or more Application SpecificIntegrated Circuits (ASICs), one or more Field Programmable Gate Array(FPGA) circuits, any other type of integrated circuit (IC), asystem-on-a-chip (SOC), and/or a state machine. Application programs725, 726, 728 may include separate modules for discrete functions suchas generating reports, providing user access to systems for suchfunctions as modifying coefficients in the predictive model and terms tobe identified by text mining software, generation of reports and otherfunctions.

Storage devices 720 may include suitable non-transitorycomputer-readable storage media, such as optical or magnetic disks,fixed disks with magnetic storage (hard drives), flash memory, tapesaccessed by tape drives, and other storage media. Processor 710communicates, such as through bus 702 and/or other data channels, withnetwork interface unit 705, system memory 730, storage devices 720 andinput/output controller 740. Via input/output controller 740, processor710 may receive data from user inputs such as pointing devices(including mice and trackballs), touch screens, audio inputs andkeyboards, and may provide data to outputs, such as data to videodrivers for formatting on displays, data to print drivers fortransmission for printing in hard copy or to image files, and data toaudio devices.

Storage devices 720 are configured to exchange data with processor 710,and may store programs containing processor-executable instructions,including instructions for accessing and filtering claims from database724, performing text mining on unstructured data extracted from database724, and applying the predictive model to structured and unstructureddata extracted from database 724, among other available functions.Processor 710 is configured to perform steps in accordance with suchprocessor-executable instructions. Processor 710 is configured to accessdata from storage devices 720, which may include connecting to storagedevices 720 and obtaining data or reading data from the storage devices,or storing new and updated data into the storage devices 720. Storagedevices 720 may include local and network accessible mass storagedevices. Storage devices 720 may include media for storing operatingsystem 722 and mass storage devices such as claim data 724 for storingdata related to claims, including identification of injured employees orother claimants, employers, structured data relating to injuries andunstructured data such as notes, and other data.

Still referring to FIG. 7, in an embodiment, inputs may include userinterfaces, including workstations having keyboards, touch screens,pointing devices such as mice and trackballs, or other user inputdevices, connected via networked communications to processor 710.Network interface unit 705 may communicate via network 750 with otherinsurance computer systems, such as claim recovery operation server 760,which may receive reports including data indicative of claims having arelatively high likelihood of subrogation according to predictive model725, and with web system server 770 to permit system access via userdevices such as tablet computer 780. In embodiments, web system server770 may be configured to generate web documents for display of resultsof application of the predictive model 725 to claim data for users, suchas claim recovery users and other analysts, and may be configured topermit suitably authorized users to review and update data such ascoefficients in the predictive model 725.

Web system server 770, or a printing and mailing system 790 and printer792 serve as a communications interface for providing reports and othercommunications to claim recovery organizations and other insurancecompany personnel. A printing and mailing system may include machineryfor printing, folding, envelope stuffing and application of postageusing automated postage meters, supplied by Neopost or other vendors.

Network interface unit 705 may further communicate with other insurancecompany computer systems, such as other computer systems maintainingdatabases relating to claims. By way of example, systems including datarelating to claims of various types, such as short term disabilityclaims and long term disability claims, may be accessed via networkinterface unit, processed using text mining software 726 and predictivemodel 725, and provided to claim recovery operation server 760 foraction by claim recovery operation staff. In embodiments, other systemshaving data relating to claimants may be accessed. By way of example,social media data stored on computer systems of social media servicesmay be accessed and included in data relating to claims analyzed usingtext mining software 726 and predictive model 725. Other third partydata relating to claimants or claims may be accessed, includinggovernment data, such as data relating to police reports and reports toother municipal government units, property ownership data, vehicleownership data, and other data.

Network 750 may be or include wired or wireless local area networks andwide area networks, and over communications between networks, includingover the Internet. Any suitable data and communication protocols may beemployed.

Referring now to FIG. 8, another exemplary embodiment of a system 800 ofthe present invention is shown. System 800 includes an insurance companyhardware server 810 which includes one or more engines or modules whichmay be utilized to perform one or more steps or functions of embodimentsof the present invention. In an embodiment, the present invention isimplemented as one or more modules of a computer software program incombination with one or more components of hardware. Such softwareprograms will be used when a system user, such as an analyst overseeinganalysis of existing claims, or an analyst comparing current text miningrules and predictive model configurations to experience of subrogationsuccess/failure, has sent a request for data or information to a serverand comprises part of the processing done on the server side of thenetwork. Such software programs may also operate on an automated basis,such as a periodic batch basis to filter and extract claim data from adatabase, access data relating to claims or claimants from third partydatabases, apply text mining to data indicative of notes or otherunstructured data, apply the predictive model, and format dataindicative of claims having a relatively high likelihood of subrogationinto reports for display, storage, printing and transmission to usersvia e-mail, upload to websites or other resources available overnetworks using suitable protocols, or otherwise.

The programs may be used in an Internet environment, where the server isa Web server and the request is formatted using HTTP (or HTTPS).Alternatively, the server may be in a corporate intranet, and extranet,or any other type of network. Use of the term “Internet” herein, whendiscussing processing associated with the user's request, includes theseother network environments, unless otherwise stated. Additionally, agraphical user interface or other module may be implemented as anintelligent hardware component incorporating circuitry including customVLSI circuits or gate arrays, off-the-shelf semiconductors such as logicchips, transistors, or other discrete components. A module may also beimplemented in programmable hardware devices such as field programmablegate arrays, programmable array logic, programmable logic devices or thelike. One or more functions of a web client or other module may beimplemented as application software in the form of a set ofprocessor-executable instructions stored in a memory of a client device,such as tablet computer 890 or laptop 885, and capable of being accessedand executed by a processor of the client device.

Referring still to FIG. 8, server 810 includes a data capture orinput/output module 815, a communications module 820, a dynamic displaygeneration or graphical user interface module 825, a data module 830,and a data validation module 835. Data module 830 is in furthercommunication with a number of databases such as claim database 850,predictive model database 852, subrogation experience database 854, andthird party database 856. Databases 850, 852, 854, 856 may beimplemented in one or more physical data storage devices incommunication with server 810, or may be implemented in remote datastorage devices accessible over one or more networks, such as cloudcomputer servers accessible via the Internet. Databases in communicationwith server 810 may include both internal and/or external/third partydatabases. By way of example, external databases may include databasesmaintained by medical care providers, health insurers, governmentagencies and social media service providers. Server 810 may beconfigured for bulk upload of data, such as bulk upload of data relatingto new claims on a daily or other periodic basis, data relating tocovered employees from an employer database, or data from medicalproviders relating to treatment provided in connection with claims. Suchdata may be furnished such as via a spreadsheet file or via suitable xmldocuments, by way of example. Data may be exchanged between server 810and one or more legacy systems via suitable middleware systems. One ormore modules, such as data validation module 835, may be configured toperform data validation steps prior to storing bulk uploaded data anddata received from legacy systems via middleware systems. Datavalidation module 835 may further serve to verify internal consistencyof data entered by one or more users. Server 810 may further beconfigured to permit bulk download of data, such as data relating toclaims identified as having a relatively high potential for subrogationfor review by a claim recovery operation.

In operation, server 810 is in communication with client devices, suchas laptop computer 885 or tablet computer 890 via network 880 whichfacilitates interaction with server 810, such as through web documents,graphical user interfaces and application programs running on clientdevices 885, 890, as shown and described herein. As used herein,devices, such as client devices 885, 890 may exchange information viaany communication network, such as a Local Area Network (LAN), aMetropolitan Area Network (MAN), a Wide Area Network (WAN), aproprietary network, a Public Switched Telephone Network (PSTN), aWireless Application Protocol (WAP) network, a Bluetooth network, awireless LAN network, and/or an Internet Protocol (IP) network such asthe Internet, an intranet, or an extranet. Note that any devicesdescribed herein may communicate via one or more such communicationnetworks.

Referring still to FIG. 8, utilizing client devices 885, 890, a properlyauthenticated system user, such as a claim recovery operation employee,or a system administrator or analyst, may access data relating to claimsand subrogation analysis. The authenticated user may also furnish datarelating to claims or subrogation experience; for example, an employeefrom a claim recovery operation may provide data relating to subrogationexperience for storing in subrogation experience database 854. Thesubrogation experience database may be employed for testing ofcoefficients and other data in the refinement and testing of thepredictive model and configuration of the text mining software. By wayof example, laptop computer 885 may be configured for remote access toserver 810 by a representative of a claim recovery operation to reviewclaims identified as having a high likelihood of subrogation. The systemmay be configured to provide a listing 886 of claims, ordered accordingto likelihood of subrogation, for review in response to a request from aclaim recovery operation for a current list of claims.

By way of further example, tablet computer 890 may be configured foraccess by an administrator, who may review and analyze subrogationlikelihood data using various data analysis and report tools 892.

A properly authenticated individual, such as an employee of an insurancecompany having administrative responsibilities, may access further dataand provide updates and modifications to data, such as updates andmodifications to predictive model data 852, such as to add or removetext and structured data and to change coefficients associated withitems of data. Such a user may also have authorization to implementupdates to processing logic employed by one or more of the modules 815,820, 825, 830, 835. In embodiments of the present invention, one or moreof the above modules, may also be implemented in combinations ofsoftware and hardware for execution by various types of computerprocessors coupled to such hardware.

Referring now to FIG. 9, user-accessible device 900 has on display 910 achart representing grouping of claims by suitability for subrogation. Inthis example, claims have been grouped into 20 groups, or vigintiles, ofequal numbers of claims in order of likelihood of subrogation. Thus,each of the 20 groups, or vigintiles, includes 5% of the claimsreviewed. Thus, line 930 representing the total number of claims incumulative vigintiles, is straight, as the total number of claims in thevigintiles increases by 5% for each vigintile. The number of groups maybe varied. The display 910 includes data representing an exemplaryexperience of selection of claims for subrogation by a claim recoveryorganization of an insurance company. The bars represent the percentageof claims in each vigintile selected for subrogation. The percentage ofclaims selected in each vigintile declines, from over 90% in the firstvigintile 920, to between 5 and 10% in the tenth vigintile 921, to onlyslightly above 0% in the twentieth vigintile 922. Line 932 representsthe cumulative percentage of total claims selected for subrogation. Ascan be seen, over 95% of selected claims are contained in the first tenvigintiles.

The grouping of claims into groups of the same numbers, ranked in orderof likelihood of subrogation as determined by the methods and systemsdescribed in this application, may further be employed in analysis ofclaims. For example, the change over periodic reviews of a claim fromgroup to group may be indicative of likelihood of subrogation, inaddition to other factors. For example, a claim, on first review, may bedetermined to have a low to moderate likelihood of subrogation, and beassigned to the 11^(th) vigintile. On a next review, based on additionaldata relating to the claim, the claim is assigned to the 9^(th)vigintile. On a third review, based on additional data relating to theclaim, the claim is assigned to the 7^(th) vigintile. The velocity ofchange in vigintile to which a claim is assigned may be employed as afactor in determining whether to forward the claim to a cost recoveryoperation for further review for suitability for subrogation. Thus, apositive velocity in increase in vigintiles renders a claim more likelyto be forwarded for review, while a negative velocity (e.g., from a6^(th) vigintile on first review to a 7^(th) vigintile on second review)may render a claim less likely to be forwarded for review.

The groups, whether vigintiles or other groupings such as deciles orquintiles, may be used for selection of claims to submit to a claimrecovery operation for further review. By way of example, the claims inthe five highest vigintiles in each review may be selected forsubmission to the claim recovery operation.

As used herein, a module of executable code may, for instance, compriseone or more physical or logical blocks of computer instructions whichmay, for instance, be organized as an object, procedure, process orfunction. Nevertheless, the executables of an identified module need notbe physically located together, but may comprise separate instructionsstored in different locations which, when joined logically together,define the module and achieve the stated purpose for the module such asimplementing the business rules logic prescribed by the present system.In embodiments of the present invention a module of executable code maybe a compilation of many instructions, and may be distributed over twoor more different code partitions or segments, among different programs,and across two or more devices. Similarly, data, including by way ofexample claims data, third party data, subrogation experience data andpredictive model data may be identified and illustrated herein withinmodules, and may be embodied in any suitable form and organized withinany suitable type of data structure. Such data may be collected as asingle data set, or may be distributed over different locationsincluding over different storage devices, and may exist, at leastpartially, merely as electronic signals on a system and/or network asshown and described herein.

Throughout processing steps, accessed values, calculated values anddraft data, for example, may be stored in temporary memory locations,such as in RAM, and then deleted or overwritten when no longer needed.

A processor may provide the central processing unit (CPU) functions of acomputing device on one or more integrated circuits. The term“processor” may include multi-core processors and central processingunits including multiple microprocessors. The central processing unitfunctionality may be provided at one or more remote locations, such asthrough application service provider and cloud computing services.

In embodiments, a processor may provide an output signal having dataindicative of one or more data items. An output signal may be carriedeither over a suitable medium, such as wire or fiber, or wirelessly. Anoutput signal may transmit data from one device to another directly,such as over a bus of a computer system from a processor to a memorydevice, or indirectly, such as over multiple networks, and withintermediate steps of storage in a buffer or memory device andretransmission. Such an output signal may be provided by the processorto a bus of a computer system together with address data at a series ofclock intervals. The address data may designate a destination device ona bus, by way of example. In embodiments, an output signal may be asignal output from a hardware communications device of a computer systemto a network, such as a local area network, a wide area network, or anetwork of interconnected networks, such as the Internet. Output signalsmay include, by way of example, data identifying formats, fields, andcontent of fields. Signals may be compatible with any appropriateformat. For example, data may be formatted in accordance with a dataformat for insurance data, such as an ACORD compatible format, or anon-ACORD xml format. Reference to an output signal having particulardata may include one or more signals bearing the information. Multiplesignals bearing the information may include sequences of digital databearing the information interleaved with sequences of digital datarelating to other information. By way of example, a signal may bepacketized for transmission. By way of further example, an output signalmay take the form of an uncompressed digital signal or a compresseddigital signal.

A system on which the methods of embodiments of the present inventionmay be implemented includes at least one central processing computer orcomputer network server. A network server includes at least onecontroller or central processing unit (CPU or processor), at least onecommunication port or hub, at least one random access memory (RAM), atleast one read-only memory (ROM) and one or more databases or datastorage devices. All of these later elements are in communication withthe CPU to facilitate the operation of the network server. The networkserver may be configured in many different ways. For example, a networkserver may be a standalone server computer or alternatively, thefunctions of a network server may be distributed across multiplecomputing systems and architectures.

A network server may also be configured in a distributed architecture,wherein databases and processors are housed in separate units orlocations. Some such servers perform primary processing functions andcontain at a minimum, a RAM, a ROM, and a general controller orprocessor. In such an embodiment, each of these servers is attached to acommunications hub or port that serves as a primary communication linkwith other servers, client or user computers and other related devices.The communications hub or port may have minimal processing capabilityitself, serving primarily as a communications router. A variety ofcommunications protocols may be part of the system, including but notlimited to: Ethernet, SAP, SAS™, ATP, Bluetooth, GSM and TCP/IP.

Data storage devices may include hard magnetic disk drives, opticalstorage units, CD-ROM drives, or flash memory, by way of example. Datastorage devices contain databases used in processing calculationsembodied in algorithms, including data for display on client devices anddata and rules for filtering of claims, by way of example. In oneembodiment, database software creates and manages these databases.Calculations and algorithms in accordance with an embodiment of thepresent invention may be stored in storage devices and accessed andexecuted by a processor, in accordance with instructions stored incomputer-readable storage media. Such algorithms may be embodied inmodules of program code, or located in separate storage locations andidentified in program code by pointers, by way of example.

Suitable computer program code may be provided for performing numerousfunctions such as analyzing claim data, determining subrogationlikelihood, generating documents and reports that analyze and presentresults of determinations of subrogation likelihood, includingdetermining and presenting statistical data, such as grouping bysuitable segments and identifying data associated with such segments.The functions described above are merely exemplary and should not beconsidered exhaustive of the type of function which may be performed bythe computer program code of embodiments of the present invention.

The computer program code required to implement the above functions (andthe other functions described herein) can be developed by a person ofordinary skill in the art, and is not described in detail herein.

The systems described herein may be in communication with systemsincluding printing and mailing systems, computer systems of employersincluding human resources departments computer systems, computer systemsof medical providers, computer systems of other insurance companies,computer systems of social media service providers, and other computersystems.

The term “computer-readable medium” as used herein refers to any mediumthat provides or participates in providing instructions to the processorof the computing device (or any other processor of a device describedherein) for execution. Such a medium may take many forms, including butnot limited to, non-volatile media, non-transitory media, tangiblemedia, volatile media, and transmission media. Non-volatile media andtangible media include, for example, optical or magnetic disks, such asmemory. Volatile media include dynamic random access memory (DRAM),which typically constitutes the main memory. Common forms ofcomputer-readable media include, for example, a floppy disk, a flexibledisk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM,DVD, any other optical medium, punch cards, paper tape, any otherphysical medium with patterns of holes, a RAM, a PROM, an EPROM orEEPROM (electronically erasable programmable read-only memory), aFLASH-EEPROM, any other memory chip or cartridge, a carrier wave asdescribed hereinafter, or any other medium from which a computer canread.

Various forms of computer readable media may be involved in carrying oneor more sequences of one or more instructions to the processor (or anyother processor of a device described herein) for execution. Forexample, the instructions may initially be borne on a magnetic disk of aremote computer. The remote computer can load the instructions into itsdynamic memory and send the instructions over an Ethernet connection,cable line, or even telephone line using a modem. A communicationsdevice local to a computing device (or, e.g., a server) can receive thedata on the respective communications line and place the data on asystem bus for the processor. The system bus carries the data to mainmemory, from which the processor retrieves and executes theinstructions. The instructions received by main memory may optionally bestored in memory either before or after execution by the processor. Inaddition, instructions may be received via a communication port aselectrical, electromagnetic or optical signals, which are exemplaryforms of wireless communications or data streams that carry varioustypes of information.

Servers of embodiments of the present invention may also interact and/orcontrol one or more user devices or terminals. The user device orterminal may include any one or a combination of a personal computer, amouse, a keyboard, a computer display, a touch screen, LCD, voicerecognition software, or other generally represented by input/outputdevices required to implement the above functionality. The program alsomay include program elements such as an operating system, a databasemanagement system and “device drivers” that allow the processor tointerface with computer peripheral devices (e.g., a video display, akeyboard, a computer mouse, etc).

An exemplary advantage of a method and system of the present inventionis that a system that implements embodiments may identify claimssuitable for subrogation that would otherwise not have been reviewed forpossible subrogation, and may avoid inefficient use of claim recoveryoperation resources in review of claims having a very low likelihood ofsubrogation.

While particular embodiments of the invention have been illustrated anddescribed, various modifications and combinations can be made withoutdeparting from the spirit and scope of the invention, and all suchmodifications, combinations, and equivalents are intended to be coveredand claimed.

What is claimed is:
 1. A computer system, comprising: one or more datastorage devices storing: a plurality of data records, each data recordcomprising stored data, the stored data comprising: structured data andunstructured data, the unstructured data comprising data indicative ofcommunications between two or more persons; a data storage devicestoring data defining a predictive model for determining a scoreindicative of suitability of each of the plurality of data records forforwarding to an operation system, responsive to receipt of a datarecord of the plurality of data records, the predictive model configuredto perform the determination based on review of the structured data andwords and phrases extracted from the data indicative of the unstructureddata of the data record; an analysis hardware server, in communicationwith the data storage devices and comprising a processor configured to,on a recurring and dynamic basis, in each of a plurality of cycles:determine a set of data records, from the plurality of data records, forreview by the predictive model; extract words and phrases from theunstructured data associated with the determined set of data recordsusing data mining to identify words and phrases for extraction; applythe predictive model to the structured data and the extracted words andphrases from the unstructured data to determine a current-cycle scoreassociated with each of the data records in the set, and assign each ofthe data records of the set to one of a plurality of groups of equalnumbers ordered in accordance with the determined current-cycle score;for each of the data records of the set for which a score was determinedin a prior cycle, determine whether the current-cycle score is greaterthan a most-recently determined prior cycle score for the same datarecord; generate a report including one or both of data records havingno score determined in any prior cycle and data records assigned scoresin one or more prior cycles determined to have a greater current-cyclescore than the most-recently determined prior cycle score for the samedata record; determine a velocity of change in group to which each datarecord is assigned; based at least in part on the determined velocity ofchange in group to which a data record is assigned, select a pluralityof the data records for forwarding to an operation system; and forward,to the operation system, the data records selected for forwarding. 2.The computer system of claim 1, wherein the determined data set ofrecords for review comprises data records having no score determined inany prior cycle and selected data records assigned scores in one or moreprior cycles.
 3. The computer system of claim 2, wherein the selecteddata records assigned scores in one or more prior cycles comprise datarecords not forwarded to the operation system and having selected agingintervals.
 4. The computer system of claim 1, wherein the analysishardware server is configured to extract words and phrases from theunstructured data using text parsing logic to identify sentences, andtagging identified words to be associated with categories andsubcategories.
 5. The computer system of claim 1, wherein the predictivemodel comprises data values and coefficients associated with structureddata.
 6. The computer system of claim 5, wherein certain of the datavalues are negative and indicative of a reduced suitability forforwarding, and certain of the data values are positive and indicativeof an increased suitability for forwarding.
 7. The computer system ofclaim 1, wherein a positive velocity of change in group renders a datarecord more likely to be selected for forwarding, and a negativevelocity of change in group renders a data record less likely to beselected for forwarding.
 8. The computer system of claim 1, wherein theanalysis hardware server is configured to forward the data indicative ofthe data records selected for forwarding via bulk download using xmldocuments.
 9. A computer-implemented method comprising: storing, in oneor more data storage devices, a plurality of data records, each datarecord comprising stored data, the stored data comprising: structureddata and unstructured data, the unstructured data comprising dataindicative of communications between two or more persons; on a recurringand dynamic basis on a cycle, accessing, by a processor of an analysishardware server, from the one or more data storage devices, datarelating to the data records to determine a set of data records forreview; processing by the processor using data mining the unstructureddata to extract words and phrases from the unstructured data; applying,by the processor, to each of the data records of the set, a predictivemodel for determining a current-cycle score indicative of suitability ofdata records for forwarding to an operation system, the predictive modelbeing configured to perform the determination responsive to receipt ofone of the data records, based on review of the structured data andwords and phrases extracted from the unstructured data, to thestructured data and the extracted words and phrases from theunstructured data of the data record, and assigning each of the datarecords of the set to one of a plurality of groups of equal numbersordered in accordance with the determined current-cycle score;determining, by the processor, for each of the data records in the setfor which a score was determined in a prior cycle, whether thecurrent-cycle score is greater than a most recently determined priorcycle score; generating, by the processor, a report including one orboth of data records having no score determined in any prior cycle anddata records assigned scores in one or more prior cycles determined tohave a greater score than the most-recently determined prior score forthe same data record; determining, by the processor, a velocity ofchange in group to which each data record is assigned; based at least inpart on the determined velocity of change in group to which a datarecord is assigned, selecting, by the processor, a plurality of the datarecords for forwarding to an operation system; and forwarding to theoperation system, the data records selected for forwarding.
 10. Thecomputer-implemented method of claim 9, wherein the determined data setof records for review comprises data records having no score determinedin any prior cycle and selected data records assigned scores in one ormore prior cycles.
 11. The computer-implemented method of claim 10,wherein the selected data records assigned scores in one or more priorcycles comprise data records not forwarded to the operation system andhaving selected aging intervals.
 12. The computer-implemented method ofclaim 9, wherein processing the unstructured data using data miningcomprises extracting words and phrases from the unstructured data usingtext parsing logic to identify sentences, and tagging identified wordsto be associated with categories and subcategories.
 13. Thecomputer-implemented method of claim 9, wherein the predictive modelcomprises data values and coefficients associated with structured data.14. The computer-implemented method of claim 13, wherein certain of thedata values are negative and indicative of a reduced suitability forforwarding, and certain of the data values are positive and indicativeof an increased suitability for forwarding.
 15. The computer-implementedmethod of claim 9, wherein a positive velocity of change in grouprenders a data record more likely to be selected for forwarding, and anegative velocity of change in group renders a data record less likelyto be selected for forwarding.
 16. The computer-implemented method ofclaim 9, wherein forwarding the data indicative of the data recordsselected for forwarding comprises forwarding via bulk download using xmldocuments.